Search Results - (( model relation ((new algorithm) OR (bees algorithm)) ) OR ( age classification _ algorithm ))
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Flood Routing in River Reaches Using a Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm
Published 2018“…The present study attempted to develop a three-parameter Muskingum model considering lateral flow for flood routing, coupling with a new optimization algorithm namely, Improved Bat Algorithm (IBA). …”
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Fake news detection: A machine learning approach
Published 2021“…The final model chosen to be deployed was a model trained using a Multinomial Naïve Bayes algorithm.…”
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Time series forecasting of energy commodity using grey wolf optimizer
Published 2015“…The ability to model and perform decision making is an essential feature of many real-world applications including the forecasting of commodity prices.In this study, a forecasting model based on a relatively new Swarm Intelligence (SI) behaviour, namely Grey Wolf Optimizer (GWO), is developed for short term time series forecasting.The model is built upon data obtained from the West Texas Intermediate (WTI) crude oil and gasoline price.Performance of the GWO model is compared against two other models which are developed based on Evolutionary Computation (EC) algorithms, namely the Artificial Bee Colony (ABC) and Differential Evolution (DE).Results showed that the GWO model outperformed DE in both crude oil and gasoline price forecasting. …”
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Conference or Workshop Item -
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Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway
Published 2023“…Parameter estimation is a challenging task in the biological process due to the complexity and nonlinearity of the model. Therefore, we propose the Artificial Bee Colony algorithm (ABC) to estimate the parameters in the fermentation pathway of S. cerevisiae to obtain more accurate values. …”
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Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer
Published 2015“…Performance of the GWO model is compared against two other models which are developed based on Evolutionary Computation (EC) algorithms, namely the Artificial Bee Colony (ABC) and Differential Evolution (DE). …”
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Hybrid Artificial Bees Colony algorithms for optimizing carbon nanotubes characteristics
Published 2018“…Optimization is a crucial process to select the best parameters in single and multi-objective problems for manufacturing process.However,it is difficult to find an optimization algorithm that obtain the global optimum for every optimization problem.Artificial Bees Colony (ABC) is a well-known swarm intelligence algorithm in solving optimization problems.It has noticeably shown better performance compared to the state-of-art algorithms.This study proposes a novel hybrid ABC algorithm with β-Hill Climbing (βHC) technique (ABC-βHC) in order to enhance the exploitation and exploration process of the ABC in optimizing carbon nanotubes (CNTs) characteristics.CNTs are widely used in electronic and mechanical products due to its fascinating material with extraordinary mechanical,thermal,physical and electrical properties. …”
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Thesis -
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Pelvic classification based on deep learning algorithm on clinical CT scans in Malaysian population
Published 2023“…Pelvis bone is the most trustworthy part in human body for sex estimation and age classification. In this research, Phenice method will be utilised for the sex estimation and age classification. …”
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Thesis -
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Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram
Published 2021“…The supervised machine learning algorithm, SVM and Decision Tree are used for the estimation of the mangrove age into young and mature. …”
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Thesis -
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Optimal timber transportation planning in tropical hill forest using bees algorithm
Published 2022“…This study proposed a multi-objective linear programming model with Bees algorithm (BA) to find an optimal cost TTP for extraction, forest road, and landing locations. …”
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Thesis -
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BONE AGE ANALYSIS FROM BONE X-RAY
Published 2018“…To achieve the objectives, the bone age classification method will involve image segmentation of the x-ray, feature extraction from the segmented images and lastly, classification of bone age level. …”
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Final Year Project -
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An image-based children age range verification and classification based on facial features angle distribution and face shape elliptical ratio
Published 2017“…The method was tested on FG-NET aging database. The classification of children from adults and the verification of children age range are implemented using SVM and Multi-SVM classification process. …”
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Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal
Published 2017“…ACO classification accuracy is compared to Genetic Algorithm classifier which also a wrapper method. …”
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Development of Low Cost Heart Rate Monitoring Device and Classification Technique Using Fuzzy Logics Algorithm
Published 2016“…This would let end users (physicians/Caregivers) to have a real-time heart rate monitoring without a need of connecting wires from the patient ward/room to the remote station which was designed in MATLAB GUI. The classification of the signal being obtained is achieved through fuzzy logics algorithm inside the MATLAB Fuzzy Logic Toolbox. …”
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Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique
Published 2024“…To ease water scarcity, dynamic programming, stochastic dynamic programming, and heuristic algorithms have been applied to solve problem matters related to water resources. …”
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Optimisation of support vector machine hyperparameters using enhanced artificial bee colony variant to diagnose breast cancer
Published 2023“…This algorithm named JAABC5ROC is the enhancement of Artificial Bee Colony (ABC) variant, JA-ABC5 by combining with Rate of Change (ROC)\. …”
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Thesis -
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Image Classification for Age-related Macular Degeneration Screening Using Hierarchical Image Decompositions and Graph Mining
Published 2011“…The resulting decomposition is then stored in a tree structure to which a weighted frequent sub-tree mining algorithm is applied. The identified sub-graphs are then incorporated into a feature vector representation (one vector per image) to which classification techniques can be applied. …”
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